The SCAR TRIPTM Initiative DICOM Katherine P

The SCAR TRIPTM Initiative & DICOM Katherine P. Andriole Society for Computer Applications in Radiology PACS Clinical Coordinator University of California at San Francisco Department of Radiology Laboratory for Radiological Informatics and Department of Bioengineering University of California at Berkeley UC LRI SF

OUTLINE u The Problem u The SCAR TRIPTM Initiative u Historical Review –Imaging in Other Fields vs Medicine » Entertainment Industry, Do. D & NASA UC LRI SF

OUTLINE u Concepts Involved –Human Perception, Image Processing, Visualization, Navigation, Usability, Standards, Databases, Integration, Evaluation, Validation UC LRI SF

OUTLINE u Affected Processes –Interpretation, Communication, Workflow & Efficiency, Diagnostic Accuracy, Quality of Care u Role of / Impact on DICOM –Incorporated but not widely used concepts –Necessary new features & functionality UC LRI SF

The Problem u u Information & Image Data Overload Requires medical image interpretation paradigm shift to evaluate, manage & exploit the massive amounts of data acquired for improved –Efficiency –Accuracy –Survival UC LRI SF

The SCAR TRIP Initiative TM Transforming the Radiological Interpretation Process u to spearhead research, education, & discovery of innovative solutions to address the problem of information & image data overload. UC LRI SF

SCAR TRIP Initiative TM u Radiology must shift its image interpretation & management processes to deal with the burgeoning medical image data sets acquired by digital imaging devices. UC LRI SF

SCAR TRIP Initiative TM u Will foster interdisciplinary research on technological, environmental & human factors to better manage & exploit the massive amount of data. UC LRI SF

SCAR TRIP Initiative TM u Will focus on: –Improving efficiency of interpretation –Improving timeliness & effectiveness –Decreasing medical errors u Goal is to improve the quality & safety of patient care. UC LRI SF

Historical Review – Why Is Medicine So Far Behind? (Do. D, NASA, Hollywood) u Special & Challenging Environment –Urgency of Results –Safety Limitations & Restrictions –Cost of Error –Tremendous Variability of Human Data within & between Individuals. UC LRI SF

Why Is Medicine So Far Behind? u Special & Challenging Environment –Difficult to Validate Performance –Poor Understanding of Human Perception & its Relationship to the Art of Medicine. UC LRI SF

Why Is Medicine So Far Behind? u Slower Adoption of Technology in General –Cultural & Practicality Barriers –More Difficult to See Clinical Impact Initially –Interdisciplinary Nature of the Solution UC LRI SF

Often there is a disconnect between Scientist-Researchers & End-Users in the Clinical Arena UC LRI SF

Enabling Technologies (creating urgency for TRIPTM) u Computing & Networking Capabilities –“Real-Time” Processing –Increased Bandwidth & Ubiquitous Access u Visualization Technologies – 3 -D Rendering, Color, Motion UC LRI SF

Enabling Technologies u Digital Imaging Modalities –True 3 -D Data Acquisition & Isotropic Voxels u More Intuitive Graphical User Interfaces –Although much more needs to be done UC LRI SF

Concepts Involved u Human Perception u Image Processing & CAD u Visualization u Navigation – Usability u Standards, Databases & Integration u Evaluation & Validation UC LRI SF

Human Perception u Develop a Standard for Image Quality u Develop Objective Methodologies & Criteria –From which to determine optimal presentation parameters –Based on Diagnostic Performance u Develop Display Standards UC LRI SF

Psychophysical Models for Detection of Abnormalities u Define & Develop Optimal Presentation Parameters by understanding –What is desired by the observer –What properties of radiological images are most useful in their interpretation –How can these properties be enhanced to improve accuracy of interpretation. UC LRI SF

DICOM Role u WG 11: Display Function Standard –Gray Scale Std Display Function GSDF –Presentation-LUT u IHE: Consistent presentation of images u AAPM TF 18: Image Quality, QA u Still must address Clinical Correspondence UC LRI SF

Image Processing & CAD u Man-Machine Systems for Image-Based Diagnosis which take advantage of both human & machine capabilities. –Relinquish more routine chores to the computer. –Have human concentrate on judgment & comprehension tasks. UC LRI SF

Image Processing & CAD u Develop Computer Aids for Feature Perception –Cuing, Overlay & Annotation u Develop Radiology Workstation of the Future –Implement computer aids into a broadly supportive workstation. –Decision Support, Data Mining & Reference Libraries UC LRI SF

Image Processing & CAD –Design a workstation that can grow to accommodate future computer tools & advances. –Support clinical, research & teaching needs. UC LRI SF

DICOM Role u u Image processing capabilities at the PACS display are currently very minimal. Processing typically done at the modality and/or required specialty workstations. How can DICOM pass image processing parameters without disclosing proprietary information? Structured Reporting & CAD (WG 8 & 15) UC LRI SF

Visualization u Static Film u Dynamic Soft Copy & Image Manipulation u Tile Mode u Stack or Cine Mode u Linked Stack Mode for 3 -D Correspondence u Multimodality Image Fusion UC LRI SF

UC LRI SF

Combining Functional & Anatomical Information UC LRI SF

3 D Spectra Anatomy Overlay “Normal” Tumor Necrosis Courtesy Cynthia Chin, M. D. , UCSF UC LRI SF

Visualization u Maximum Intensity Projection u Multi-Planar Reconstruction u 3 -D Surface/Volume Rendering u Virtual Reality Representations u ? ? ? UC LRI SF

CT Cholangiogram - Axial Courtesy Richard S. Breiman, M. D. , UCSF UC LRI SF

Sliding MIP Bile Duct Anomalies missed by MRCP in potential partial liver donors. Courtesy Richard S. Breiman, M. D. , UCSF UC LRI SF

3 -D Surface/Volume Rendering Courtesy Gary R. Caputo, M. D. , UCSF UC LRI SF

Courtesy Cynthia Chin, M. D. , UCSF UC LRI SF

DICOM Role u Currently most 3 -D representations must be – processed on specialty workstations –some must be saved as screen-capture –manually push to PACS workstations & Enterprise-wide Web (if capable of displaying) –Raw data often not stored. UC LRI SF

DICOM Role u u How can DICOM pass 3 D Model without disclosing proprietary information? How simplify interoperability? –Unify Architecture UC LRI SF

DICOM Role u DICOM conceived as a strategy for moving & storing collections of single images. –Network utilization is suboptimal u PACS must accommodate multiple images which can be treated as a single unit –Series-Awareness, 3 D, 4 D, Functional Sets, Cross-Referencing of Objects & Fusion u Unified presentation of Color WG 11 & others. UC LRI SF

DICOM Role u WG 16, Supplement 49 defines multiframe (MR) images; model for CT; WG 17, 20, 21. –enhanced image storage SOP class –allows multiple images to be combined into one instance –Raw Data –Dimensionality –Context Info UC LRI SF

Navigation & Usability u 3 -D & Motion u Virtual Reality – Fly-Throughs u Hand-Eye Cues u Hand-Helds for Point-of-Care Delivery u Context Matching u Voice Activation u ? ? ? UC LRI SF

3 -D Surface Rendering CABG Courtesy Gary R. Caputo, M. D. , UCSF UC LRI SF

Virtual Reality Fly-Through of Coronary Arteries Courtesy Gary R. Caputo, M. D. , UCSF UC LRI SF

Sliding VR Courtesy Richard S. Breiman, M. D. , UCSF UC LRI SF

Michael Teistler, Technical Institute of Braunschweig UC LRI SF

Hand-Helds for Point-of Care Delivery UC LRI SF

DICOM Role u Navigation by radiologist/clinician at the PACS display (or enterprisewide web) in real-time –Raw Data & Processing Model –Color Encoding –Overlays –Waveforms –Audio or Other Sense? UC LRI SF

Standards, Databases & Integration u Open Standards u Real-Time Processing at PACS Display u 3 -D Integrated into PACS Display & Web u Other Relevant Data – Integrated HISRIS-PACS-Speech & IHE (maintaining user & patient focus) UC LRI SF

Evaluation & Validation u Objective Methodologies u Standard Datasets for Performance Testing u Collaborative & Comparison Research UC LRI SF

Affected Processes u Interpretation u Communication u Workflow & Efficiency u Diagnostic Accuracy –Reduction of Medical Errors u Quality of Care UC LRI SF

We Have Come a Long Way, But… UC LRI SF

What SCAR Hopes To Do u Bring Forward the Problem u Facilitate Exchange of Ideas –Between Researchers, End-Users, Industry, Other Fields –Via Workshops & Forums –By Lobbing NIH & Other Agencies u Sponsor Research u Communicate Issues & Results UC LRI SF

DICOM Role (especially) WG 4 Compression WG 10 Strategic WG 8 Structured Reporting WG 11 Display Function Std WG 16 Magnetic Resonance, Sup 49 WG 17 3 D WG 20 Imaging & Information Systems Integration WG 21 Computed Tomography UC LRI SF

DICOM Role Join in the TRIP! UC LRI SF